Complete Guide to Using Pydantic: Data Validation Made Easy

Pydantic is a powerful Python library for data validation and settings management using Python type hints. It's extremely popular in the modern Python ecosystem, especially used by FastAPI, and has become the de-facto standard for data validation.

In this tutorial, we'll learn Pydantic from basics to advanced usage with practical examples.

Why Pydantic?

Pydantic Advantages:

Installation

Install Pydantic v2 (Latest)

# Install Pydantic v2
pip install pydantic

Install with email validation
pip install "pydantic[email]"

Install with dotenv support
pip install "pydantic-settings"

Verify Installation

import pydantic
print(f"Pydantic version: {pydantic.version}")

Should print 2.x.x

Basic Usage: Your First Model

1. Simple Model

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str
    age: int

Create instance
user = User(id=1, name="Ruby", email="ruby@rubythalib.ai", age=25)

print(user)
Output: id=1 name='Ruby' email='ruby@rubythalib.ai' age=25

print(user.modeldump())

Output: {'id': 1, 'name': 'Ruby', 'email': 'ruby@rubythalib.ai', 'age': 25}

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    name: str
    price: float
    instock: bool

Strings will be converted to int and float
product = Product(
    id="123",           # String -> int
    name="Laptop",
    price="15000000",   # String -> float
    instock="yes"      # String -> bool

)

print(product.id, type(product.id))
Output: 123 

print(product.price, type(product.price))
Output: 15000000.0 

print(product.instock, type(product.instock))

Output: True 

from pydantic import BaseModel, ValidationError

class User(BaseModel):
    id: int
    name: str
    age: int

try:
    user = User(id="notanumber", name="Ruby", age=25)

except ValidationError as e:
    print(e)
    # Will print detailed error about invalid id

1 validation error for User
id
  Input should be a valid integer, unable to parse string as an integer [type=intparsing, inputvalue='notanumber', inputtype=str]

Field Types and Constraints

1. Optional Fields

from typing import Optional
from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: Optional[str] = None  # Optional with default None
    phone: str | None = None     # Python 3.10+ syntax

user1 = User(id=1, name="Ruby")
print(user1.email)  # None

user2 = User(id=2, name="Ahmad", email="ahmad@example.com")
print(user2.email)  # ahmad@example.com

2. Default Values

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    name: str
    price: float = 0.0
    category: str = "General"
    instock: bool = True


product = Product(id=1, name="Notebook")
print(product.modeldump())
{'id': 1, 'name': 'Notebook', 'price': 0.0, 'category': 'General', 'instock': True}

from pydantic import BaseModel, Field

class User(BaseModel):
    id: int = Field(gt=0, description="User ID must be positive")